TOPS: A Framework for Trusted Opinion Analysis of Product Reviews Using Hybrid Deep Learning Based D2CL Filter T. K. Balaji, Annushree Bablani, S. R. Sreeja, Hemant Misra Expert Systems, 2025 The rapid growth of online product reviews has made it increasingly challenging for consumers to make informed purchase decisions. However, the abundance of reviews, including fake or augmented and sarcastic reviews, poses a challenge for consumers. To address this challenge, this paper introduces the TOPS (Trusted Opinion analysis of Product reviewS) framework, a novel approach that leverages a hybrid deep learning‐based D2CL (Dual Deep leaning based cleaning) filter to enhance the reliability of online reviews. The proposed methodology employs the D2CL filter to identify and eliminate fake and sarcastic reviews, ensuring that the consolidated sentiment analysis provides users with trustworthy opinions. The framework is equipped with the R‐mGRU, a hybrid deep learning model specifically designed to tackle the nuances of product reviews. This model has demonstrated impressive accuracy rates, achieving 89%, 91%, and 94% for fake, sarcasm, and sentiment analysis tasks, respectively. The TOPS framework makes a significant contribution to improving the overall quality and authenticity of product reviews, empowering consumers with more reliable information for informed decision‐making in online shopping scenarios.
A Two-stage Hybrid Lossless EEG Data Compression Technique for Fog-assisted IoHT Rama Krushna Rath, Rupalin Nanda, Annushree Bablani, Sreeja S.R. Proceedings of 2025 IEEE 22nd India Council International Conference Indicon 2025, 2025 This work presents a hybrid lossless compression technique for Electroencephalogram (EEG) data, featuring clustering and encoding stages in fog enabled Internet of Healthcare Things (IoHT) network. With the recent advancement in the EEG devices, multichannel EEG sensors generate huge volume of patients' data, emphasizing the necessity for low-latency transmission in IoHT networks. Recently, fog-assisted healthcare networks have played a significant role in efficient data management and analysis. This paper proposes fog-enabled Hybrid Lossless EEG data Compression method, termed as HLoECo, employing a lossless approach that achieves strong compression performance. The proposed technique is tested on two real-time EEG datasets. Dataset-1, the Bonn University dataset, notably gives enhanced average compression ratio of 8.72 and a maximum compression power of 93%. Further, the simulation involves thorough analysis of Z subject using k-means clustering across various K values (ranging between 10 to 190). Dataset-2, the Physionet Motor Movement/Imagery dataset, exhibits 97% compression power and a maximum compression ratio of 38.50 on applying the proposed technique. Moreover, HLoECo is performed on five distinct evaluation metrics and compared with both conventional and recent EEG data compression techniques. Furthermore, the work illustrates the relationship between codec time and compressed data size for all the techniques. The finding shows that the proposed technique outperforms all other compared methods and is suitable for low-latency healthcare deployments.
3D-POS: 3D Pareto Optimized Head Selection for Fog-enabled Smart EEG Healthcare IoT Edara Sri Sai Kaushal, Rama Krushna Rath, S.R. Sreeja, Abhishek Hazra Indiscon 2025 IEEE 6th India Council International Subsections Conference Proceedings, 2025 This paper proposes a master head selection framework for fog-enabled EEG-based smart Healthcare Internet of Things (HIoT) systems. In a smart healthcare environment where multiple fog devices handle multi-channel EEG data, selecting an optimal fog device as the processing head is critical to ensure low delay, efficient energy consumption, and reliable data handling. Given the heterogeneous nature of fog devices in real-world settings, we formulate the head selection as a multi-objective optimization problem, considering the key factors such as delay, energy, computation capacity, and proximity to EEG devices. By employing Pareto optimization techniques, the framework identifies a set of non-dominated head candidates among all the fog devices that achieve the best trade-offs among conflicting objectives across various scenarios. The simulation results demonstrate that the proposed approach significantly minimizes $10-50 \%$ delay compared to existing methods under varying workloads and different configurations of EEG and fog devices. This work enhances the responsiveness and sustainability of fog-enabled IoT infrastructures by intelligently balancing delay, energy consumption, and computational efficiency.
Classification of Motor Imagery based EEG signals using Ensemble model Saathvika Bandi, Venkata Sai Kasyap J, S. R. Sreeja, Annushree Bablani 2024 3rd International Conference for Innovation in Technology Inocon 2024, 2024 The Brain-Computer Interfaces (BCIs) has been a source of fascination since its discovery. Controlling items just by thinking about them is a new degree of modernity. Out of many paradigms present in electroencephalogram (EEG), motor imagery (MI) has gained significance for being a safe and non-invasive process and including cognitive engagement. The main challenge for MI-based BCIs is Feature extraction and training a classifier, which could give us better accuracy. In our research, we adhere to the standard MI-based BCI workflow, but our methodology introduces an optimized and better model for classifying EEG data. To reduce the data used for the classification, we extracted five features, spanning both frequency and wavelet domain features, from the EEG signals. These features are then dimensionally reduced by passing through Linear Discriminant Analysis (LDA), and the best two features have been selected for classification. This Ensemble classifier model is applied to the best two features that have been extracted. This result is being compared with existing traditional Machine Learning algorithms - Support Vector Machines (SVM) and Logistic Regression (LR). Our empirical findings highlight the remarkable boost in classification accuracy achieved by the ensemble model compared to the tried-and-true machine learning methods. The suggested approach can be further enhanced to create a dependable and real-time BCI application.
BCI Augmented text entry mechanism for people with special needs Sreeja S.R., Vaidic Joshi, Shabnam Samima, Anushri Saha, Joytirmoy Rabha, Baljeet Singh Cheema, Debasis Samanta, Pabitra Mitra Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
RECENT SCHOLAR PUBLICATIONS
A Two-stage Hybrid Lossless EEG Data Compression Technique for Fog-assisted IoHT RK Rath, R Nanda, A Bablani, SR Sreeja IEEE 22nd India Council International Conference (INDICON) , 2025 2025
A Lossless Healthcare Data Compression Approach using Near-Edge Computing RK Rath, SR Sreeja, A Hazra, R Nanda Industry 5.0 Key Technologies and Drivers, 69-78 , 2025 2025 Citations: 3
Motorcycle Violation Detection for Intelligent Transportation Systems During Night Time L Andavarapu, B Pradeep Karri, J Naik Ramavath, SR Sreeja 2025 International Conference on Emerging Techniques in Computational … , 2025 2025
3D-POS: 3D Pareto Optimized Head Selection for Fog-enabled Smart EEG Healthcare IoT ESS Kaushal, RK Rath, SR Sreeja, A Hazra 2025 IEEE 6th India Council International Subsections Conference (INDISCON) , 2025 2025 Citations: 1
TOPS: a framework for trusted opinion analysis of product reviews using hybrid deep learning based D2CL filter TK Balaji, A Bablani, SR Sreeja, H Misra Expert Systems 42 (2), e13765 , 2025 2025 Citations: 3
Sarcovid: A framework for sarcasm detection in tweets using hybrid transfer learning techniques TK Balaji, A Bablani, SR Sreeja, H Misra International Conference on Pattern Recognition, 1-12 , 2025 2025 Citations: 4
Sentiment and sarcasm: Analyzing gender bias in sports through social media with deep learning S Praveen, B TK, Sreeja SR, A Bablani ICON 2024, 132-138 , 2024 2024
SASE: Sentiment Analysis with Aspect Specific Evaluation Using Deep Learning with Hybrid Contextual Embedding TK Balaji, A Bablani, SR Sreeja, H Misra 20th International Conference on Distributed Computing and Intelligent … , 2024 2024 Citations: 1
2dp-fhs: 2d pareto optimized fog head selection for multiple eeg healthcare data analysis and computations SH Kurra, RK Rath, SR Sreeja International Conference on Advances in Computing and Data Sciences, 58-68 , 2024 2024 Citations: 5
MINDSCOPE: machine-learning inferencing of NeuroData for seamless cognitive overload prediction and evaluation R Katinni, SR Sreeja, A Bablani 2024 Citations: 1
Classification of motor imagery based EEG signals using ensemble model S Bandi, SR Sreeja, A Bablani 2024 3rd International Conference for Innovation in Technology (INOCON), 1-6 , 2024 2024 Citations: 1
Classification of Motor Imagery based EEG Signals Using Deep Learning Architecture V Sai Kasyap J, S Bandi, SR Sreeja, SK Satapathy 2023 IEEE 20th India Council International Conference (INDICON), 806-811 , 2024 2024
Sensecor: A framework for COVID-19 variants severity classification and symptoms detection TK Balaji, A Bablani, SR Sreeja, H Misra Evolving Systems 15 (1), 65-82 , 2024 2024 Citations: 2
TSOSVNet: Teacher-student collaborative knowledge distillation for Online Signature Verification CS V, A Gautam, V P, SR Sreeja, RKS G Proceedings of the IEEE/CVF International Conference on Computer Vision … , 2023 2023
MS3A: Wrapper-Based Feature Selection with Multi-swarm Salp Search Optimization R Shathanaa, SR Sreeja, E Elakkiya Advances in Data-driven Computing and Intelligent Systems: Selected Papers … , 2023 2023
Moment Centralization based Gradient Descent Optimizers for Convolutional Neural Networks S Sadu, SR Dubey, SR Sreeja Computer Vision and Machine Intelligence 586, 51 - 63 , 2023 2023 Citations: 3
Descent Optimizers for Convolutional S Sadu, SR Dubey, SR Sreeja Computer Vision and Machine Intelligence: Proceedings of CVMI 2022, 51 , 2023 2023
Dictionary Learning and Greedy Algorithms for removing Eye Blink Artifacts from EEG Signals SR Sreeja, S Rajmohan, MS Sodhi, D Samanta, P Mitra Circuits, Systems, and Signal Processing , 2023 2023 Citations: 6
Multi-cohort whale optimization with search space tightening for engineering optimization problems S Rajmohan, E Elakkiya, SR Sreeja Neural Computing and Applications 35 (12), 8967-8986 , 2023 2023 Citations: 17
Dictionary Reduction in Sparse Representation-based Classification of Motor Imagery EEG Signals SR Sreeja, D Samanta Multimedia Tools and Applications , 2023 2023 Citations: 4
MOST CITED SCHOLAR PUBLICATIONS
Motor Imagery EEG Signal Processing and Classification using Machine Learning Approach SR Sreeja, D Samanta, P Mitra, M Sarma Jordanian Journal of Computers and Information Technology (JJCIT) 4 (02), 80-93 , 2018 2018.0 Citations: 81
Motor imagery EEG signal processing and classification using machine learning approach Sreeja SR, J Rabha, KY Nagarjuna, D Samanta, P Mitra, M Sarma 2017 International Conference on New Trends in Computing Sciences (ICTCS), 61-66 , 2017 2017.0 Citations: 81
Removal of Eye Blink Artifacts from EEG Signals using Sparsity SR Sreeja, RR Sahay, D Samanta, P Mitra IEEE Journal of Biomedical and Health Informatics 22 (5), 1362 - 1372 , 2017 2017.0 Citations: 77
Classification of multiclass motor imagery EEG signal using sparsity approach SR Sreeja, D Samanta Neurocomputing 368, 133-145 , 2019 2019.0 Citations: 53
Classification of EEG signals for cognitive load estimation using deep learning architectures A Saha, V Minz, S Bonela, SR Sreeja, R Chowdhury, D Samanta International Conference on Intelligent Human Computer Interaction, 59-68 , 2018 2018.0 Citations: 42
Classification of motor imagery based EEG signals using sparsity approach SR Sreeja, J Rabha, D Samanta, P Mitra, M Sarma International Conference on Intelligent Human Computer Interaction, 47-59 , 2017 2017.0 Citations: 41
Distance-based weighted sparse representation to classify motor imagery EEG signals for BCI applications SR Sreeja, Himanshu, D Samanta Multimedia Tools and Applications , 2020 2020.0 Citations: 36
A deep learning approach to automated sleep stages classification using multi-modal signals SK Satapathy, HK Kondaveeti, SR Sreeja, H Madhani, N Rajput, D Swain Procedia Computer Science 218, 867-876 , 2023 2023.0 Citations: 30
BCI augmented text entry mechanism for people with special needs SR Sreeja, V Joshi, S Samima, A Saha, J Rabha, BS Cheema, ... Intelligent Human Computer Interaction: 8th International Conference, IHCI … , 2017 2017.0 Citations: 18
Multi-cohort whale optimization with search space tightening for engineering optimization problems S Rajmohan, E Elakkiya, SR Sreeja Neural Computing and Applications 35 (12), 8967-8986 , 2023 2023.0 Citations: 17
An automated approach for task evaluation using EEG signals V Anand, SR Sreeja, D Samanta arXiv preprint arXiv:1911.02966 , 2019 2019.0 Citations: 9
Weighted sparse representation for classification of motor imagery EEG signals SR Sreeja, Himanshu, D Samanta, M Sarma 2019 41st Annual International Conference of the IEEE Engineering in … , 2019 2019.0 Citations: 9
Dictionary Learning and Greedy Algorithms for removing Eye Blink Artifacts from EEG Signals SR Sreeja, S Rajmohan, MS Sodhi, D Samanta, P Mitra Circuits, Systems, and Signal Processing , 2023 2023.0 Citations: 6
2dp-fhs: 2d pareto optimized fog head selection for multiple eeg healthcare data analysis and computations SH Kurra, RK Rath, SR Sreeja International Conference on Advances in Computing and Data Sciences, 58-68 , 2024 2024.0 Citations: 5
An automated system for sleep staging using EEG brain signals based on a machine learning approach SK Satapathy, HK Kondaveeti, SR Sreeja IEEE INDICON 2022, 1-6 , 2022 2022.0 Citations: 5
Intelligent Human Computer Interaction A Saha, V Minz, S Bonela, SR Sreeja, R Chowdhury, D Samanta Springer , 0 Citations: 5
Sarcovid: A framework for sarcasm detection in tweets using hybrid transfer learning techniques TK Balaji, A Bablani, SR Sreeja, H Misra International Conference on Pattern Recognition, 1-12 , 2025 2025.0 Citations: 4
Dictionary Reduction in Sparse Representation-based Classification of Motor Imagery EEG Signals SR Sreeja, D Samanta Multimedia Tools and Applications , 2023 2023.0 Citations: 4
Opinion mining on COVID-19 vaccines in India using deep and machine learning approaches TK Balaji, A Bablani, SR Sreeja 2022 International Conference on Innovative Trends in Information Technology … , 2022 2022.0 Citations: 4
A Lossless Healthcare Data Compression Approach using Near-Edge Computing RK Rath, SR Sreeja, A Hazra, R Nanda Industry 5.0 Key Technologies and Drivers, 69-78 , 2025 2025.0 Citations: 3
